Research and application of a hybrid system based on interpolation for forecasting direct economic losses of marine disasters

Zhao, Xin; Li, Hui; Ding, Lili; Liu, Mengxi

doi:10.1016/j.ijdrr.2019.101121

Cited by 11 publications

(10 citation statements)

References 41 publications

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“…Zhao found that the cubic spline interpolation is the most potent interpolation for forecasting the direct economic losses of marine disasters among the four interpolations tested [ 18 ]. Therefore, we chose the cubic spline interpolation as one of the experiment baselines.…”

Section: Methodsologymentioning

confidence: 99%

“…Ensemble learning is a widely-used algorithm [13][14][15][16] that combines several machine learning techniques into an ensemble model to reduce deviation and improve prediction accuracy [17]. Zhao et al [18] used an ensemble learning model Adaboost-BPNN for forecasting direct economic losses of marine disasters. Besides, ensemble learning is rarely used in this field.…”

Section: Introductionmentioning

confidence: 99%

“…However, it is unrealistic and impossible to obtain large amounts of storm surge data from the historical literature due to the limited number of storm surges in practice. Zhao et al [ 18 ] adapted four interpolation methods to tackle the small sample issue, but the interpolation methods in this article cannot reflect the disaster processes’ randomness. Besides, few studies are focusing on the data scarcity problem in this scope.…”

Section: Introductionmentioning

confidence: 99%

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A Data Augmentation-Based Evaluation System for Regional Direct Economic Losses of Storm Surge Disasters

Sun

Wang

2021

IJERPH

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The accurate prediction of storm surge disasters’ direct economic losses plays a positive role in providing critical support for disaster prevention decision-making and management. Previous researches on storm surge disaster loss assessment did not pay much attention to the overfitting phenomenon caused by the data scarcity and the excessive model complexity. To solve these problems, this paper puts forward a new evaluation system for forecasting the regional direct economic loss of storm surge disasters, consisting of three parts. First of all, a comprehensive assessment index system was established by considering the storm surge disasters’ formation mechanism and the corresponding risk management theory. Secondly, a novel data augmentation technique, k-nearest neighbor-Gaussian noise (KNN-GN), was presented to overcome data scarcity. Thirdly, an ensemble learning algorithm XGBoost as a regression model was utilized to optimize the results and produce the final forecasting results. To verify the best-combined model, KNN-GN-based XGBoost, we conducted cross-contrast experiments with several data augmentation techniques and some widely-used ensemble learning models. Meanwhile, the traditional prediction models are used as baselines to the optimized forecasting system. The experimental results show that the KNN-GN-based XGBoost model provides more precise predictions than the traditional models, with a 64.1% average improvement in the mean absolute percentage error (MAPE) measurement. It could be noted that the proposed evaluation system can be extended and applied to the geography-related field as well.

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Section: Methodsologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Data Augmentation-Based Evaluation System for Regional Direct Economic Losses of Storm Surge Disasters

Sun

Wang

2021

IJERPH

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“…Based on the availability, the monthly data collection has been prioritised and later in case of absence of monthly data the yearly data has been interpolated over the selected period in various ways based on the characteristics of the data. Because of the interpolation of the yearly data, which is a frequent practice in forecasting (Zhao et al, 2019;Tokumitsu et al, 2015) and even in simulation and data generation (Li et al, 2020), the outliers in the data set have been eroded automatically before starting the analysis. Aligning or removing outliers as a logical pre-processing step of data analysis brings in benefits by avoiding a few bad apples that may spoil the entire bushel (Cant and Xu, 2020; Shah and Patil, 2019;Lyutikova and Shmatova, 2020).…”

Section: Econophysics Methodologymentioning

confidence: 99%

An econophysics approach to forecast bulk shipbuilding orderbook: an application of Newton’s law of gravitation

Sakalayen

Duru

Hirata

2020

MABR

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Purpose Bulk shipping mostly facilitates the smooth flow of raw materials around the globe. Regardless, forecasting a bulk shipbuilding orderbook is a seldom researched domain in the academic arena. This study aims to pioneer an econophysics approach coupled with an autoregressive data analysis technique for bulk shipbuilding order forecasting. Design/methodology/approach By offering an innovative forecasting method, this study provides a comprehensive but straightforward econophysics approach to forecast new shipbuilding order of bulk carrier. The model has been evaluated through autoregressive integrated moving average analysis, and the outcome indicates a relatively stable good fit. Findings The outcomes of the econophysics model indicate a relatively stable good fit. Although relevant maritime data and its quality need to be improved, the flexibility in refining the predictive variables ensure the robustness of this econophysics-based forecasting model. Originality/value By offering an innovative forecasting method, this study provides a comprehensive but straightforward econophysics approach to forecast new shipbuilding order of bulk carrier. The research result helps shipping investors make decision in a capital-intensive and uncertainty-prone environment.

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“…Coastal zones are often characterized by high population densities and economic activities, which make them particularly vulnerable to typical disasters such as storm surges and rough seas (Haran 2020). Currently, preventing marine disasters is an important research topic (Zhao et al 2019), and developing a deep understanding of disaster characteristics is critical to allowing policy makers to take proper measures when managing marine disasters (Su and Yang 2018). China has traditionally been vulnerable to many natural disasters (Yi 2012).…”

Section: Introductionmentioning

confidence: 99%

Temporal-spatial characteristics of storm surges and rough seas in coastal areas of Mainland China from 2000 to 2019

Wang

Liu

et al. 2021

Nat Hazards

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Marine disasters pose a serious threat to economic and social development; therefore, understanding their occurrence rhythms is of great importance to disaster prevention and mitigation. As a major form of marine disaster in China, storm surges and rough seas are particularly worthy of attention. In this study, statistical data regarding storm surges and rough seas over the past 20 years were collected, and a visual approach was utilized to detect their scope, distribution, and temporal-spatial characteristics. Implementation of disaster prevention and mitigation was then discussed. The results revealed the following: (1) storm surges exhibited significant seasonality (occurring in summer and autumn), while rough sea occurrences occurred throughout the year. ( 2) The losses caused by storm surges showed clear regional differences. Specifically, the economic losses and death tolls in southern provinces were greater than in northern provinces, but they decreased significantly from 2000-2009 to 2010-2019. (3) The loss caused by rough seas also showed regional differences, with greater loss values in the southern provinces than northern provinces. The total loss has dropped significantly in recent years.

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Research and application of a hybrid system based on interpolation for forecasting direct economic losses of marine disasters

Cited by 11 publications

References 41 publications

A Data Augmentation-Based Evaluation System for Regional Direct Economic Losses of Storm Surge Disasters

A Data Augmentation-Based Evaluation System for Regional Direct Economic Losses of Storm Surge Disasters

An econophysics approach to forecast bulk shipbuilding orderbook: an application of Newton’s law of gravitation

Temporal-spatial characteristics of storm surges and rough seas in coastal areas of Mainland China from 2000 to 2019

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